Although savanna ecosystems cover approximately 20 % of the terrestrial land surface and can have productivity equal to some closed forests, their role in the global carbon cycle is poorly understood. This study explored the applicability of a past spaceborne Lidar mission and the potential of future missions to estimate canopy height and carbon storage in these biomes.
The research used data from two Oak savannas in California, USA: the Tejon Ranch Conservancy in Kern County and the Tonzi Ranch in Santa Clara County. In the first paper we used non-parametric regression techniques to estimate canopy height from waveform parameters derived from the Ice Cloud and land Elevation Satellite’s Geoscience Laser Altimeter System (ICESat-GLAS) data. Merely adopting the methods derived for forests did not produce adequate results but the modeling was significantly improved by incorporating canopy cover information and interaction terms to address the high structural heterogeneity inherent to savannas. Paper 2 explored the relationship between canopy height and aboveground biomass. To accomplish this we developed generalized models using the classical least squares regression modeling approach to relate canopy height to above ground woody biomass and then employed Hierarchical Bayesian Analysis (HBA) to explore the implications of using generalized instead of species composition-specific models. Models that incorporated canopy cover proxies performed better than those that did not. Although the model parameters indicated interspecific variability, the distribution of the posterior densities of the differences between composition level and global level parameter values showed a high support for the use of global parameters, suggesting that these canopy height-biomass models are universally (large scale) applicable.
As the spatial coverage of spaceborne lidar will remain limited for the immediate future, our objective in paper 3 was to explore the best means of extrapolating plot level biomass into wall-to-wall maps that provide more ecological information. We evaluated the utility of three spatial modeling approaches to address this problem: deterministic methods, geostatistical methods and an image segmentation approach. Overall, the mean pixel biomass estimated by the 3 approaches did not differ significantly but the output maps showed marked differences in the estimation precision and ability of each model to mimic the primary variable’s trend across the landscape. The results emphasized the need for future satellite lidar missions to consider increasing the sampling intensity across track so that biomass observations are made and characterized at the scale at which they vary.
We used data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne photon counting lidar sensor developed by NASA Goddard to simulate ICESat-2 data. We segmented each transect into different block sizes and calculated canopy top and mean ground elevation based on the structure of the histogram of the block’s aggregated photons. Our algorithm was able to compute canopy height and generate visually meaningful vegetation profiles at MABEL’s signal and noise levels but a simulation of the expected performance of ICESat-2 by adjusting MABEL data's detected number of signal and noise photons to that predicted using ATLAS instrument model design cases indicated that signal photons will be substantially lower. The lower data resolution reduces canopy height estimation precision especially in areas of low density vegetation cover.
Given the clear difficulties in processing simulated ATLAS data, it appears unlikely that it will provide the kind of data required for mapping of the biophysical properties of savanna vegetation. Rather, resources are better concentrated on preparing for the Global Ecosystem Dynamics Investigation (GEDI) mission, a waveform lidar mission scheduled to launch by the end of this decade. In addition to the full waveform technique, GEDI will collect data from 25 m diameter contiguous footprints with a high across track density, a requirement that we identified as critically necessary in paper 3. (Abstract shortened by UMI.)
|Advisor:||Lefsky, Michael A.|
|Commitee:||Leisz, Stephen J., Ryan, Michael, Sibold, Jason|
|School:||Colorado State University|
|Department:||Ecosystem Science and Sustainability|
|School Location:||United States -- Colorado|
|Source:||DAI-B 77/01(E), Dissertation Abstracts International|
|Subjects:||Ecology, Geographic information science, Remote sensing|
|Keywords:||Biomass, Canopy height, Lidar, Savanna, Spatial modelling|
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